Watershed Geovisualization

Lesson Goals

In this lesson, we demonstrate several approaches towards a particular subset of geovisualization methods, namely choropleth maps. We start with an exploratory workflow introducing mapclassify and geopandas to create different choropleth classifications and maps for quick exploratory data analysis. We then introduce the geoviews package for interactive mapping in support of exploratory spatial data analysis.

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Prerequests

1. Install geopandas

2. install pySal

   NB:make sure you start your work with new directory with all library

PySAL Map Classifiers

import mapclassify as mc
import geopandas as gpd
import matplotlib.pyplot as plt
import contextily as cx

ethio = gpd.read_file("C:/Users/agurm/Downloads/pysal-scipy20-master/Eth GIS/eth_watshed.shp")

ethio.columns

Index(['AREA', 'PERIMETER', 'ETWSHED01C', 'BASIN_NR', 'BASIN_NAME', 'BASIN',
       'SQKM', 'BASIN_COD', 'geometry'],
      dtype='object')

ethio

	AREA	PERIMETER	ETWSHED01C	BASIN_NR	BASIN_NAME	BASIN	SQKM	BASIN_COD	geometry
0	1.503697e+09	201961.90894	110	11	Mereb	110	15208	2A1	POLYGON ((431294.827 1602480.205, 430185.938 1...
1	1.232069e+09	199722.49770	121	12	Tekeze	121	1954	3B1	POLYGON ((393200.000 1596000.000, 393127.219 1...
2	2.173783e+08	86924.09546	233	23	Danakil	233	550	14D1	POLYGON ((585026.438 1604424.500, 584441.750 1...
3	7.157738e+08	162011.35318	111	11	Mereb	111	1201	2B6	POLYGON ((542535.082 1605247.134, 542817.563 1...
4	1.637719e+09	260200.47067	110	11	Mereb	110	15208	2A1	POLYGON ((503860.813 1617412.625, 503959.125 1...
...	...	...	...	...	...	...	...	...	...
653	2.113343e+08	70832.95709	212	21	Awash	212	211	12C16	POLYGON ((558000.000 1026000.000, 558478.250 1...
654	2.088036e+08	64158.26747	212	21	Awash	212	208	12C17	POLYGON ((554800.000 1021200.000, 555600.563 1...
655	7.490104e+07	46678.58684	212	21	Awash	212	74	12C18	POLYGON ((554000.000 1020400.000, 553986.563 1...
656	8.817818e+08	132929.29581	212	21	Awash	210	881	12C13	POLYGON ((673200.000 1025600.000, 673236.000 1...
657	2.913601e+10	896494.14814	990	21	Awash	211	29136	9999	POLYGON ((747713.625 1225637.875, 748922.438 1...

658 rows × 9 columns

f, ax = plt.subplots(1, figsize=(12, 8))
ethio.plot(column='BASIN', scheme='QUANTILES', ax=ax,
        edgecolor='white', legend=True, linewidth=0.3)
ax.set_axis_off()
plt.show()

As a first cut, geopandas makes it very easy to plot a map quickly. If you know the area well, this may do fine for quick exploration. If you don't know a place extremely well (or you want to make a figure easy to understand for those who don't) it's often a good idea to add a basemap for context. We can do that easily using the contextily package

ethio.fillna(ethio.mean(), inplace = True) 

f, ax = plt.subplots(1, figsize=(12, 8))
ethio.plot(column='BASIN', scheme='QUANTILES', alpha=0.6, ax=ax, legend=True)
cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
ax.set_axis_off()
plt.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-709446de3aa7> in <module>
      1 f, ax = plt.subplots(1, figsize=(12, 8))
      2 ethio.plot(column='BASIN', scheme='QUANTILES', alpha=0.6, ax=ax, legend=True)
----> 3 cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
      4 ax.set_axis_off()
      5 plt.show()

AttributeError: 'NoneType' object has no attribute 'to_string'

hv = ethio['BASIN']

mc.Quantiles(hv, k=5)

Quantiles               

    Interval       Count
------------------------
[110.00, 131.00] |   156
(131.00, 141.00] |   116
(141.00, 162.00] |   136
(162.00, 181.00] |   122
(181.00, 990.00] |   128

mc.Quantiles(hv, k=10)

Quantiles               

    Interval       Count
------------------------
[110.00, 124.00] |    73
(124.00, 131.00] |    83
(131.00, 132.00] |    51
(132.00, 141.00] |    65
(141.00, 152.00] |    62
(152.00, 162.00] |    74
(162.00, 172.00] |    82
(172.00, 181.00] |    40
(181.00, 212.00] |    70
(212.00, 990.00] |    58

q10 = mc.Quantiles(hv, k=10)

dir(q10)

['__call__',
 '__class__',
 '__delattr__',
 '__dict__',
 '__dir__',
 '__doc__',
 '__eq__',
 '__format__',
 '__ge__',
 '__getattribute__',
 '__gt__',
 '__hash__',
 '__init__',
 '__init_subclass__',
 '__le__',
 '__lt__',
 '__module__',
 '__ne__',
 '__new__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__setattr__',
 '__sizeof__',
 '__str__',
 '__subclasshook__',
 '__weakref__',
 '_classify',
 '_fmt',
 '_set_bins',
 '_summary',
 '_table_string',
 '_update',
 'adcm',
 'bins',
 'classes',
 'counts',
 'find_bin',
 'fmt',
 'gadf',
 'get_adcm',
 'get_fmt',
 'get_gadf',
 'get_legend_classes',
 'get_tss',
 'k',
 'make',
 'name',
 'plot',
 'set_fmt',
 'table',
 'tss',
 'update',
 'y',
 'yb']

q10.bins

array([124., 131., 132., 141., 152., 162., 172., 181., 212., 990.])

q10.counts

array([73, 83, 51, 65, 62, 74, 82, 40, 70, 58], dtype=int64)

fj10 = mc.FisherJenks(hv, k=10)

fj10

FisherJenks             

    Interval       Count
------------------------
[110.00, 125.00] |    75
(125.00, 135.00] |   176
(135.00, 153.00] |    86
(153.00, 164.00] |    80
(164.00, 175.00] |   100
(175.00, 192.00] |    28
(192.00, 220.00] |    60
(220.00, 252.00] |    29
(252.00, 281.00] |    15
(281.00, 990.00] |     9

fj10.adcm

1372.0

q10.adcm

8611.0

bins = [100000, 500000, 1000000, 1500000]

ud4 = mc.UserDefined(hv, bins=bins)
ud4

UserDefined                     

        Interval           Count
--------------------------------
[    110.00,  100000.00] |   658
( 100000.00,  500000.00] |     0
( 500000.00, 1000000.00] |     0
(1000000.00, 1500000.00] |     0

GeoPandas: Choropleths

f, ax = plt.subplots(1, figsize=(12, 8))
ethio.plot(column='BASIN', scheme='QUANTILES', ax=ax, alpha=0.6, legend=True)

cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
ax.set_axis_off()
plt.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-21-81b7d92baa5e> in <module>
      2 ethio.plot(column='BASIN', scheme='QUANTILES', ax=ax, alpha=0.6, legend=True)
      3 
----> 4 cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
      5 ax.set_axis_off()
      6 plt.show()

AttributeError: 'NoneType' object has no attribute 'to_string'

f, ax = plt.subplots(1, figsize=(12, 8))
ethio.plot(column='BASIN', scheme='FisherJenks', ax=ax,
        alpha=0.6, legend=True)

cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)

ax.set_axis_off()
plt.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-22-4a9ba35c4cf1> in <module>
      3         alpha=0.6, legend=True)
      4 
----> 5 cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
      6 
      7 ax.set_axis_off()

AttributeError: 'NoneType' object has no attribute 'to_string'

f, ax = plt.subplots(1, figsize=(12, 8))
ethio.plot(column='BASIN', scheme='FisherJenks', ax=ax,
        alpha=0.6, legend=True, cmap='Blues')

cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)

ax.set_axis_off()
plt.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-23-df102f128a29> in <module>
      3         alpha=0.6, legend=True, cmap='Blues')
      4 
----> 5 cx.add_basemap(ax, crs=ethio.crs.to_string(), source=cx.providers.Stamen.TonerLite)
      6 
      7 ax.set_axis_off()

AttributeError: 'NoneType' object has no attribute 'to_string'

import numpy as np

import geoviews as gv
import geoviews.feature as gf
import xarray as xr
from cartopy import crs
import hvplot.pandas

import geopandas as gpd
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

cities.hvplot(global_extent=True, frame_height=450, tiles=True)

shp_link = 'C:/Users/agurm/Downloads/pysal-scipy20-master/Eth GIS/eth_watshed.shp'
gdf = gpd.read_file(shp_link)
gdf.fillna(gdf.mean(), inplace = True)

gdf.hvplot()

Further reading

1. Beyond choropleth maps: A review of techniques to visualize quantitative areal geodata link.

2. Dynamic Choropleth Maps - Using Amalgamation to Increase Area Perceivability link

3. Exploring the Sensitivity of Choropleths under Attribute Uncertainty link.

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Conclusion

This lesson has presented geovisualization techniques for the visualization of spatial data. In this lesson you learn the role of map classification in exploratory data analysis and producing choropleth maps. Additionally, you also learn how to use different tools include graphs representing statistical distribution of attribute values, calculation of statistical quality of a classification, and various color schemes that can be applied to represent classes on a choropleth map.